BDI-agent-based quantum-behaved PSO for shipboard power system reconfiguration

This paper presents a BDI (Belief-Desire-Intention)-agent-based Quantum-Behaved Particle Swarm Optimisation (QPSO) reconfiguration method for shipboard zonal power systems. Shipboard zonal power systems are founded on navy ships, and desired to be highly reconfigurable. Since shipboard power system reconfiguration may change its topology, and load priority should be taken into consideration, this makes shipboard reconfiguration into a non-linear distributed optimisation problem. Specially, switches in the shipboard zonal power system are modelled as intelligent BDI agents. This paper uses swarm intelligence to realise BDI-agent reasoning and optimise its reconfiguration objective. To verify the effectiveness of the proposed approach, comparative simulations are conducted on the method without reconfiguration and reconfiguration method based on PSO/QPSO. Simulation results show that the reconfiguration strategy can achieve success in realising shipboard power system service restoration cases which demonstrate the feasibility and the advantages of the proposed BDI-agent-based QPSO reconfiguration method.

[1]  Men-Shen Tsai,et al.  Development a BDI-Based Intelligent Agent Architecture for Distribution Systems Restoration Planning , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[2]  Emiliano Lorini,et al.  Introducing Relevance Awareness in BDI Agents , 2009, PROMAS.

[3]  Chengfeng Jian,et al.  A particle swarm optimisation algorithm for cloud-oriented workflow scheduling based on reliability , 2014, Int. J. Comput. Appl. Technol..

[4]  Yongji Wang,et al.  Application of Multi-agent and Genetic Algorithm in Network Reconfiguration of Ship Power System , 2012 .

[5]  M.A.L. Badr,et al.  Distribution system reconfiguration using a modified Tabu Search algorithm , 2010 .

[6]  Anurag K. Srivastava,et al.  Multi-agent based reconfiguration of AC-DC shipboard distribution power system , 2010, Integr. Comput. Aided Eng..

[7]  Wenbo Xu,et al.  A Novel and More Efficient Search Strategy of Quantum-Behaved Particle Swarm Optimization , 2007, ICANNGA.

[8]  Angelo Corallo,et al.  A knowledge-based decision support system for shipboard damage control , 2012, Expert Syst. Appl..

[9]  Abhinav Sadu,et al.  A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch , 2011 .

[10]  Lin Padgham,et al.  A BDI agent programming language with failure handling, declarative goals, and planning , 2011, Autonomous Agents and Multi-Agent Systems.

[11]  Xianyong Feng,et al.  Multi-Agent System-Based Real-Time Load Management for All-Electric Ship Power Systems in DC Zone Level , 2012, IEEE Transactions on Power Systems.

[12]  Malabika Basu,et al.  Modified particle swarm optimization for nonconvex economic dispatch problems , 2015 .

[13]  Haibo Zhang,et al.  Shipboard systems deploy automated protection , 1998 .

[14]  Juan Lin,et al.  Multi-agent simulated annealing algorithm based on differential perturbation for protein structure prediction problems , 2015, Int. J. Comput. Appl. Technol..

[15]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[16]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[17]  Chun-Lien Su,et al.  Design of a multi-agent system for shipboard power systems restoration , 2014, 2014 IEEE/IAS 50th Industrial & Commercial Power Systems Technical Conference.

[18]  Jing Zhang,et al.  Research on PSO algorithms for the rectangular packing problem , 2015, Int. J. Comput. Appl. Technol..

[19]  Michael E. Bratman,et al.  Intention, Plans, and Practical Reason , 1991 .

[20]  Ahmed R. AbulWafa.  A new heuristic approach for optimal reconfiguration in distribution systems , 2011 .

[21]  Attia A. El-Fergany,et al.  Synergy of a genetic algorithm and simulated annealing to maximize real power loss reductions in transmission networks , 2014 .

[22]  Salima Ouadfel,et al.  Bio-inspired algorithms for multilevel image thresholding , 2014, Int. J. Comput. Appl. Technol..